Deterministic behavior of temperature field in turboprop engine via shallow neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F21%3A00351654" target="_blank" >RIV/68407700:21220/21:00351654 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s00521-021-06013-7" target="_blank" >https://doi.org/10.1007/s00521-021-06013-7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-021-06013-7" target="_blank" >10.1007/s00521-021-06013-7</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deterministic behavior of temperature field in turboprop engine via shallow neural networks

Popis výsledku v původním jazyce

A study of machine learning approaches for temperature field (rake) prediction in a turboprop engine is presented. The potential of supervised machine learning and shallow feed-forward neural network (NN) architectures is studied to predict and reconstruct temperature fields based on a single sensor measurement. The reason to study shallow (not deep nor gated) NN first before more complex networks is that shallow NN can provide us with robust nonlinear mapping at a low computational cost. Simultaneously, their mathematical structures can be simple, especially for a limited amount of training data. Thus, revealing a governing law in data via a simpler architecture is desirable. Further, the problem suits feed-forward architectures as only one temperature sensor is considered input in real time. It is investigated which type of shallow NN architectures with which learning algorithm can be most accurate with the best generalization for provided turboprop temperature field data. The important finding is that it is possible to capture the deterministic governing law of temperatures in a turboprop engine. Thus, the temperature sensor locations in rakes can be analyzed to allocate the important positions for a limited number of temperature sensors inside the engine. The machine learning results also confirm the importance of slow heat transfer between internal engine parts and temperature sensors alongside the air propulsion. Thus, the proposed neural network application concept is promising as a funding base for further design of modern turboprop health monitoring system.

Název v anglickém jazyce

Deterministic behavior of temperature field in turboprop engine via shallow neural networks

Popis výsledku anglicky

A study of machine learning approaches for temperature field (rake) prediction in a turboprop engine is presented. The potential of supervised machine learning and shallow feed-forward neural network (NN) architectures is studied to predict and reconstruct temperature fields based on a single sensor measurement. The reason to study shallow (not deep nor gated) NN first before more complex networks is that shallow NN can provide us with robust nonlinear mapping at a low computational cost. Simultaneously, their mathematical structures can be simple, especially for a limited amount of training data. Thus, revealing a governing law in data via a simpler architecture is desirable. Further, the problem suits feed-forward architectures as only one temperature sensor is considered input in real time. It is investigated which type of shallow NN architectures with which learning algorithm can be most accurate with the best generalization for provided turboprop temperature field data. The important finding is that it is possible to capture the deterministic governing law of temperatures in a turboprop engine. Thus, the temperature sensor locations in rakes can be analyzed to allocate the important positions for a limited number of temperature sensors inside the engine. The machine learning results also confirm the importance of slow heat transfer between internal engine parts and temperature sensors alongside the air propulsion. Thus, the proposed neural network application concept is promising as a funding base for further design of modern turboprop health monitoring system.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20304 - Aerospace engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Computing and Applications

ISSN

0941-0643

e-ISSN

1433-3058

Svazek periodika

33

Číslo periodika v rámci svazku

19

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

17

Strana od-do

13145-13161

Kód UT WoS článku

000698073600001

EID výsledku v databázi Scopus

2-s2.0-85115187144